Christian Quadri

Vehicle platooning is expected to significantly increase road utilization while reducing transport cost and driver fatigue. Of course, the larger the platoon size the higher the efficiency. However, long platoons generate challenging road conditions when, for instance, platoons must allow for cross traffic at intersections, roundabouts and highway junctions. In this paper, we study the performance of platoons that split and merge in response to the traffic and road context. We propose PLATO, an edge-based control system to efficiently manage the resulting set of coordinated sub-platoons, which acts as a platoon of platoons, i.e., a multi-platoon. We analyze costs and benefits of a multi-platoon by means of analysis and realistic simulations. We show that what is most critical for good performance is the coherence of the instructions received by individual platoons, and the optimal adaptation of the number of sub-platoons in response to the changing road and traffic conditions.

In designing the city of the future, city managers and urban planners are driven by specific citizens’ behaviors. In fact, economic and financial behaviors, and specifically, which goods and services citizens purchase and how they allocate their spending, are playing a central role in planning targeted services. In this context, cashless payments provide an invaluable data source to identify such spending behaviors. In this work, we propose a methodology to extract the consumption behaviors of a large sample of customers through credit card transaction data. The main outcome of the methodology is a concise representation of the economic behavior of people residing in a city, the so-called city consumption profile. We inferred the city consumption profile from a network-based representation of the similarity among the customers in terms of purchase allocation; on top of which we applied a community detection algorithm to identify the representative consumption profiles. By applying the above methodology to a set of credit card transactions of an Italian financial group, we showed that cities, even geographically close, exhibit different profiles which makes them unique. Specifically, usage patterns focused on a single type of good/service—mono-categorical consumption profile—are the main factors leading to the differences in the city profiles. Our analysis also showed that there is a group of consumption profiles common to all cities, made up by purchases of primary goods/services, such as food or clothing. In general, the city consumption profile represents a tool for understanding the economic behaviors of the citizens and for comparing different cities. Moreover, city planners and managers may use it in the outline of city services tailored to the citizens’ needs.

The theme of human mobility is transversal to multiple fields of study and applications, from ad hoc networks to smart cities, from transportation planning to recommendation systems on social networks. Despite the considerable efforts made by a few scientific communities and the relevant results obtained so far, there are still many issues only partially solved that ask for general and quantitative methodologies to be addressed. A prominent aspect of scientific and practical relevance is how to characterize the mobility behavior of individuals. In this article, we look at the problem from a location-centric perspective: We investigate methods to extract, classify, and quantify the symbolic locations specified in telco trajectories and use such measures to feature user mobility. A major contribution is a novel trajectory summarization technique for the extraction of the locations of interest, i.e., attractive , from symbolic trajectories. The method is built on a density-based trajectory segmentation technique tailored to telco data, which is proven to be robust against noise. To inspect the nature of those locations, we combine the two dimensions of location attractiveness and frequency into a novel location taxonomy, which allows for a more accurate classification of the visited places. Another major contribution is the selection of suitable entropy-based metrics for the characterization of single trajectories, based on the diversity of the locations of interest. All these components are integrated in a framework utilized for the analysis of 100,000+ telco trajectories. The experiments show how the framework manages to dramatically reduce data complexity, provide high-quality information on the mobility behavior of people, and finally succeed in grasping the nature of the locations visited by individuals.